Fellow members of the Math Department,

There will be a Virtual CAM Seminar today @ 3:30.

The pertinent information is below.

Best,
Abner

=================================

Hi there, 

Abner J. SalGATO is inviting you to a scheduled Zoom meeting. 

Topic: CAM Seminar
Time: May 4, 2022 03:30 PM Eastern Time (US and Canada) 

Join from PC, Mac, Linux, iOS or Android: 
https://tennessee.zoom.us/j/95760710360

Or iPhone one-tap (US Toll):  +13126266799,95760710360#  or
+16468769923,95760710360# 

Or Telephone:
    Dial:
    +1 312 626 6799 (US Toll)
    +1 646 876 9923 (US Toll)
    +1 301 715 8592 (US Toll)
    +1 346 248 7799 (US Toll)
    +1 669 900 6833 (US Toll)
    +1 253 215 8782 (US Toll)
    Meeting ID: 957 6071 0360
    International numbers available: 
https://tennessee.zoom.us/u/aBDkHdOU3

Or an H.323/SIP room system:
    H.323: 162.255.37.11 (US West) or 162.255.36.11 (US East) 
    Meeting ID: 957 6071 0360

    SIP: [log in to unmask]

=====================
Speaker: Harbir Antil, George Mason University
Host: Xiaobing Feng
 

Title: Optimization Based Deep Neural Networks 

Abstract:

This talk will introduce novel deep neural networks (DNNs) as
constrained optimization problems. In particular, the talk will
introduce DNNs with memory, which helps overcome the vanishing gradient
challenge. The talk will also explore reducing the computational
complexity of DNNs by  introducing a bias ordering. Approximation
properties of the DNNs will also be discussed. 

These proposed DNNs will be shown to be excellent  surrogates to
parameterized (nonlinear) partial differential equations (PDEs),
Bayesian inverse problems, and data  assimilation problems, with
multiple advantages over the traditional approaches. The DNNs will also
be applied to chemically reacting flow problems. The latter requires
solving a system of stiff ODEs and fluid flow equations. These are
highly challenging problems, for instance, for combustion the number of
reactions can be significant (over 100). Due to the large CPU
requirements of chemical reactions (over 99% of total CPU time), a
large number of flow and combustion problems are presently beyond the
capabilities of even the largest supercomputers.


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